Large Language Models with Semantic Search

What You'll Learn

• Enhance keyword search using Cohere Rerank.
• Use embeddings to leverage dense retrieval, a powerful NLP tool.
• Evaluate your effectiveness for further optimization.

About This Course

This course teaches essential techniques to integrate large language models (LLMs) into search, going beyond traditional keyword-based methods. You will learn to elevate user experience through dense retrieval and reranking, enhancing relevance and efficiency in search applications.

• Understand and implement basic keyword search, foundational to many search systems.
• Enhance search with rerank, which ranks results by relevance to the query.
• Use embeddings for dense retrieval, performing search based on semantic meaning for vastly improved results.
• Gain practical experience working with large datasets and managing challenges in search accuracy.
• Learn to implement LLM-powered search for websites and other projects.

By the end, you’ll have the skills needed to create effective, modern search systems using LLMs.

Course Outline

• Introduction
  Overview of semantic search and the advantages of using large language models.
• Keyword Search
  Implementation of basic keyword search functionality, with code examples.
• Embeddings
  Learn to use embeddings for capturing the semantic meaning of text, enhancing retrieval.
• Dense Retrieval
  Integrate dense retrieval techniques to improve search accuracy by focusing on semantic relevance.
• ReRank
  Implement Cohere’s Rerank to refine search results by ranking based on query relevance.
• Generating Answers
  Build an answer generation function to respond to user queries based on indexed data.
• Conclusion
  Recap of concepts and steps to integrate LLMs for search and retrieval tasks.

Who Should Join?

This course is designed for those with basic Python knowledge who wish to understand LLM foundations and explore using semantic search.